Scottish Flag

Abstract

Executive Summary

This report examines drug misuse trends in Scotland from 1996 to 2023, highlighting key findings:

Trends: Drug misuse deaths peaked in 2020 (1,339 deaths) but remained high at 1,276 in 2023.
Causes: Accidental poisoning is now the leading cause of death, with poly-drug use contributing to over 70% of fatalities.
Disparities: Individuals in the most deprived quintile experience drug death rates up to 8 times higher than those in the least deprived quintile.
Policy Failures: Governance issues and inequitable funding exacerbate the crisis, despite targeted harm reduction measures like naloxone distribution.

Key Recommendations

Expand harm reduction services, including supervised injection sites and mobile units.
Scale trauma-informed, gender-sensitive services to address systemic inequities.
Integrate addiction and mental health care, focusing on deprived areas.

Introduction

The drug misuse crisis in Scotland remains one of the most severe public health challenges in Europe (McAuley, Matheson, and Robertson, 2022).

According to Scotland’s National Records, substance abuse caused 1,276 recorded deaths in 2023. At 22.4 fatalities per 100,000, Scotland’s drug death rate far exceeds the UK’s average of 7.6, reflecting ongoing systemic failures despite recent declines.

This paper examines drug misuse trends in Scotland from 1996 to 2023, focusing on:
- Geographic, socioeconomic, and gender disparities
- Policy interventions (e.g., the 2011 reform of reporting standards)
- The role of poly-drug use in fatalities

The analysis identifies temporal trends, disparities, and policy implications while offering actionable recommendations to reduce drug-related deaths in Scotland.

How to Use This Report

This report integrates interactive plots and maps to explore Scotland’s drug misuse crisis:

Hover over data points for additional information.
Use zoom and pan features in visualizations to examine specific time periods or categories.
Click on lines in the legend to hide them from the graph, making it easier to focus on specific data trends.

Methods

This report utilizes data from Scotland’s National Records (1996–2023), integrating demographic, geographic, and substance-related factors.

Data cleaning, visualization, and statistical analysis were conducted using:
- ggplot2
- dplyr
- plotly

Interactive elements were added to enhance user engagement with the data.

Results and Analysis

This section explores Scotland’s drug misuse crisis through:
1. Statistical trends
2. Substance group analysis
3. Key disparities

Setting Up Environment

Load Libraries

# Load libraries
library(readxl)
library(dplyr)
library(ggplot2)
library(janitor)
library(here)
library(tidyr)
library(openxlsx)
library(sf)
library(plotly)
library(leaflet)
library(htmltools)  # Load the htmltools package
library(RColorBrewer)

Set Up File Path

file_path <- "C:/Users/alexa/OneDrive/Documents/year 4/forensics/essay/drug-related-deaths-scotland/drug-related-deaths-23-data.xlsx"

Trends in Drug Misuse Over Time

Drug-related deaths (DRDs) in Scotland rose from 244 in 1996 to a peak of 1,339 in 2020, before declining slightly to 1,276 in 2023 (Figure 1). These trends reflect the evolving challenges of tackling drug misuse, especially the impact of benzodiazepines and opioids.

The COVID-19 pandemic exacerbated vulnerabilities by disrupting support services, increasing social isolation, and tripling loneliness for those facing mental health challenges (O’Sullivan et al., 2021).

Key Takeaways:

Benzodiazepines: Sharp rise post-2015, particularly street benzos.
Opioids: Persistent contributors to drug misuse mortality.
Policy Influence: The 2011 Naloxone Programme showed measurable impact.
Pandemic Impact: Increased isolation amplified risks.

Figure 1: Trends in Drug Misuse Deaths in Scotland by Gender (1996–2023)

The dashed navy line marks a notable policy change in 2011, which corresponds to the implementation of the National Naloxone Programme.
Male deaths consistently exceeded female deaths, highlighting gender disparities in drug misuse trends.
The peak in total deaths occurred in 2020, with both genders contributing to the surge.
Post-2020, a slight decline in drug-related deaths has been observed, but the numbers remain alarmingly high.

Key takeaway: Gender-specific vulnerabilities must be addressed to tackle the crisis effectively. While men are disproportionately affected, the systemic barriers faced by women—such as stigma and caregiving stress—must also be addressed.

# Load and process data, create high-res data for the policy line in one step
gender_all_data <- read.xlsx(file_path, sheet = "Table_1", startRow = 6) %>%
  clean_names() %>%
  select(year, sex, drug_misuse_deaths) %>%
  rename(`Total Deaths` = drug_misuse_deaths)

# Create a high-resolution data frame for the dashed line (only once)
policy_line <- data.frame(
  x = rep(2011, 100),  # Fixed x-value for the policy year
  y = seq(0, max(gender_all_data$`Total Deaths`), length.out = 100),  # Fine-grained y-values
  text = "Policy Change: A notable policy change occurred in 2011."  # Tooltip text for dashed line
)

# Create the base ggplot with gender lines, points, and policy line
gender_all_plot <- ggplot(gender_all_data) +
  # Add gender lines and points
  geom_line(aes(x = year, y = `Total Deaths`, color = sex, group = sex), linewidth = 1.2) +
  geom_point(aes(x = year, y = `Total Deaths`, color = sex), size = 3) +
  # Add red dashed line
  geom_line(data = policy_line, aes(x = x, y = y, text = text), color = "navy", linetype = "dashed", size = 1) +
  labs(
    title = "Drug Misuse Deaths in Scotland (1996–2023)",
    x = "Year",
    y = "Total Deaths",
    color = "Gender"
  ) +
  scale_color_manual(
    values = c("Females" = "#66c2a5", "Males" = "#8da0cb", "Persons" = "#fc8d62")  # Custom colors for each gender
  ) +
  theme_minimal() +
  theme(
    legend.position = "bottom",  # Keep legend at the bottom
    legend.title = element_text(size = 12, face = "bold"),  # Larger legend title
    legend.text = element_text(size = 10),  # Larger legend text
    plot.title = element_text(size = 16, face = "bold"),  # Bold and larger title
    axis.title = element_text(size = 12),
    axis.text = element_text(size = 10)
  )

# Convert the ggplot object to plotly, adding the red dashed line as a separate trace
interactive_gender_all_plot <- ggplotly(gender_all_plot, tooltip = c("x", "y", "color")) %>%
  plotly::add_trace(
    x = policy_line$x,
    y = policy_line$y,
    mode = "lines",
    line = list(dash = "dash", color = "navy", width = 2),
    text = policy_line$text,
    hoverinfo = "text",  # Show only the custom text
    showlegend = FALSE  # Don't add to the legend
  )

# View the plot
interactive_gender_all_plot

Note: The policy change in 2011 aligns with the introduction of Scotland’s National Naloxone Programme, a critical intervention in reducing opioid-related fatalities.

Figure 2: Trends in Drug Misuse Deaths in Scotland by Substance Group (1996–2023)

Benzodiazepines showed a dramatic rise post-2015, becoming the leading contributor to drug misuse deaths by 2020.
Opioid-related deaths have shown persistent increases, further driving Scotland’s alarming drug death rates.
Deaths involving stimulants and other substances remained relatively lower but have exhibited steady growth over the years.

Key takeaway: The emergence of high-potency illicit substances such as ‘street benzos’ highlights the importance of tailored interventions and enhanced harm reduction strategies to mitigate the crisis.

# Load and clean the data
table_3 <- read.xlsx(file_path, sheet = "Table_3", startRow = 6) %>%
  clean_names()

# Convert Year to numeric and clean substance columns in one step
table_3_clean <- table_3 %>%
  mutate(across(
    c("any_opiate_or_opioid":"alcohol"), 
    ~ as.numeric(gsub("[^0-9.]", "", .))
  )) %>%
  mutate(year = as.numeric(gsub("[^0-9]", "", year))) %>%
  filter(!is.na(year))  # Remove non-numeric rows

# Reshape into long format, clean deaths, classify substances, and filter unwanted rows
table_3_long <- table_3_clean %>%
  pivot_longer(
    cols = c("any_opiate_or_opioid":"alcohol"),
    names_to = "substance",
    values_to = "deaths"
  ) %>%
  mutate(
    deaths = replace_na(as.numeric(deaths), 0),  # Clean deaths in one mutate step
    group = case_when(
      substance %in% c("heroin_morphine_note_6", "methadone", "buprenorphine",
                       "codeine_or_a_codeine_containing_compound", 
                       "dihydrocodeine_or_a_d_h_c_containing_compound") ~ "Opioids",
      substance %in% c("any_benzodiazepine", "any_prescribable_benzodiazepine_note_7",
                       "diazepam_note_8", "any_street_benzodiazepine_note_7",
                       "etizolam_note_8", "bromazolam_note_5_note_8") ~ "Benzodiazepines",
      substance %in% c("cocaine", "ecstasy_type", "amphetamines") ~ "Stimulants",
      substance %in% c("gabapentin_and_or_pregabalin", "alcohol", "nitazenes_note_5") ~ "Others",
      TRUE ~ "Unclassified"
    )
  ) %>%
  filter(!substance %in% c("all_drug_misuse_deaths", "single_drug_deaths", "poly_drug_deaths"),
         group != "Unclassified")

# Summarize total deaths by year and group (no further mutate necessary here)
filtered_data_clean <- table_3_long %>%
  group_by(year, group) %>%
  summarize(total_deaths = sum(deaths, na.rm = TRUE), .groups = "drop") %>%
  rename(
    Year = year,  # Rename year column for hover text
    `Total Deaths` = total_deaths  # Rename total_deaths column for hover text
  )

# Create the faceted interactive plot
grouped_facet_plot <- ggplot(filtered_data_clean, aes(x = Year, y = `Total Deaths`)) +
  geom_line(aes(color = group, group = group), size = 1) +
  geom_point(aes(color = group), size = 2) +
  labs(
    title = "Trends in Drug Misuse Deaths by Substance Group (1996–2023)",
    x = "Year",
    y = "Number of Deaths"
  ) +
  theme_minimal() +
  theme(
    legend.position = "none",  # Remove the legend
    plot.title = element_text(size = 16, face = "bold"),
    axis.title = element_text(size = 12),
    axis.text = element_text(size = 10),
    strip.text = element_text(face = "bold", size = 12)  # Bold facet titles
  ) +
  scale_color_brewer(palette = "Set2") +
  facet_wrap(~ group)

# Convert to interactive plot
interactive_facet_plot <- ggplotly(grouped_facet_plot, tooltip = c("Year", "Total Deaths", "group")) %>%
  layout(
    title = list(text = "Trends in Drug Misuse Deaths by Substance Group (1996–2023)"),
    hoverlabel = list(font = list(size = 12))
  )

interactive_facet_plot

In 2022, men accounted for 73% of drug-related deaths (DRDs), reflecting higher engagement in intravenous and poly-drug use (Gossop et al., 1994; Gjersing and Helle, 2021). The rise of ‘street benzos,’ characterised by high potency and unknown composition, has disproportionately impacted male users (McAuley et al., 2022).

Women, however, face distinct vulnerabilities, including:
- Caregiving stress
- Domestic abuse
- Stigma, compounded by welfare system changes (Tweed et al., 2022)

These disparities demand gender-sensitive harm reduction strategies that address structural inequities.

The 2011 policy change, which introduced new reporting procedures, marks a pivotal point in the timeline. Scotland implemented a National Naloxone Programme in 2011, which was associated with a 36% reduction in opioid-related deaths within four weeks of prison release.
- Specifically, the proportion of prison-release opioid-related deaths fell from 9.8% pre-2011 to 6.3% by 2013, demonstrating the program’s significant impact (Bird et al., 2016).

However, general practitioners showed limited awareness and involvement in naloxone distribution, citing:
1. A need for evidence of effectiveness
2. The necessity for appropriate training (Matheson et al., 2014)

These findings highlight the complex interplay of socioeconomic factors, healthcare services, and targeted interventions in addressing Scotland’s DRD crisis. They emphasise the importance of both structural changes and specific measures like naloxone distribution in comprehensive drug policy.

Critically, the persistent rise in deaths prior to 2011 underscores Scotland’s failure to address root causes such as:
- Socio-economic deprivation
- Inadequate mental health services

This trend highlights the need for proactive policies that address structural inequities alongside reactive measures like naloxone distribution.

Causes of Drug Misuse Deaths

Accidental poisoning emerged as the leading cause of death post-2011 (Figure 3), driven by the rising prevalence of synthetic opioids, which are 50–100 times more potent than morphine (Thompson et al., 2021).

These substances, often combined with benzodiazepines, are implicated in over 60% of drug-related deaths, highlighting the dangers of poly-drug use (McAuley et al., 2022).

Key takeaway: This increase reflects not only shifts in drug supply but also systemic shortcomings in prevention and intervention strategies.

Figure 3: Trends in Drug Misuse Deaths by Cause Over Time (1996–2023)

The dashed line marks the division between pre- and post-2011 periods, following changes in reporting practices.
Accidental poisoning has become the leading cause of death post-2011, while deaths attributed to mental disorders have decreased significantly, likely reflecting shifts in classification methods.

table_2 <- read.xlsx(file_path, sheet = "Table_2", startRow = 6) %>%
  clean_names() %>%
  # Rename columns for simplicity
  rename(
    all_drug_misuse_deaths = "all_drug_misuse_deaths", 
    mental_disorders_due_to_drugs = "mental_and_behavioural_disorders_due_to_drug_use", 
    accidental_poisoning = "accidental_poisoning", 
    intentional_self_poisoning = "intentional_self_poisoning", 
    assault_by_drugs = "assault_by_drugs_etc", 
    undetermined_intent = "undetermined_intent"
  ) %>%
  # Convert 'year' to numeric and filter out NA years
  mutate(year = as.numeric(year)) %>%
  filter(!is.na(year))  # Remove non-numeric rows

#Pivot to long format 
table_2_long <- table_2 %>%
  pivot_longer(
    cols = c("mental_disorders_due_to_drugs", "accidental_poisoning", 
             "intentional_self_poisoning", "assault_by_drugs", 
             "undetermined_intent"), 
    names_to = "cause_of_death", 
    values_to = "deaths"
  ) %>%
  mutate(
    deaths = as.numeric(deaths),
    deaths = replace_na(deaths, 0),
    period = ifelse(year < 2011, "Pre-2011", "Post-2011"),
    cause_of_death = recode(
      cause_of_death,
      "mental_disorders_due_to_drugs" = "Mental Disorders",
      "accidental_poisoning" = "Accidental Poisoning",
      "intentional_self_poisoning" = "Self-Poisoning",
      "assault_by_drugs" = "Assault",
      "undetermined_intent" = "Undetermined"
    ),
    hover_text = paste(
      "Year:", year,
      "<br>Deaths:", deaths,
      "<br>Cause of Death:", cause_of_death
    )
  )

# Create a high-resolution data frame for the policy line
policy_line <- data.frame(
  x = rep(2011, 100),  # Fixed x-value for the policy year
  y = seq(0, max(table_2_long$deaths, na.rm = TRUE), length.out = 100),  # Fine-grained y-values
  text = "Policy Change: A notable policy change occurred in 2011."  # Tooltip text
)

# Create the base ggplot with `color` for compatibility
combined_plot <- ggplot() +
  # Add lines and points for causes of death
  geom_line(data = table_2_long, aes(x = year, y = deaths, color = cause_of_death, group = cause_of_death, text = hover_text), size = 1) +
  geom_point(data = table_2_long, aes(x = year, y = deaths, color = cause_of_death, text = hover_text), size = 2) +
  # Add the policy line
  geom_line(data = policy_line, aes(x = x, y = y), linetype = "dashed", color = "navy", size = 0.8) +
  labs(
    title = "Drug Misuse Deaths by Cause Over Time (1996–2023)",
    subtitle = "Dashed line marks the division between Pre- and Post-2011 periods.",
    x = "Year",
    y = "Number of Deaths",
    color = "Cause of Death"  # Use "color" instead of "colour"
  ) +
  theme_minimal() +
  theme(
    legend.position = "bottom",
    legend.title = element_text(size = 12, face = "bold"),
    legend.text = element_text(size = 10),
    plot.title = element_text(size = 16, face = "bold"),
    plot.subtitle = element_text(size = 12),
    axis.title = element_text(size = 12),
    axis.text = element_text(size = 10)
  ) +
  scale_color_brewer(palette = "Set2")

# Convert the base plot to plotly
interactive_combined_plot <- ggplotly(combined_plot, tooltip = c("text"))

# Add the red dashed line as a separate trace
red_line_trace <- list(
  x = policy_line$x,
  y = policy_line$y,
  mode = "lines",
  line = list(dash = "dash", color = "navy", width = 2),
  text = policy_line$text,
  hoverinfo = "text",  # Show only the custom text
  showlegend = FALSE  # Don't add to the legend
)

# Add the red dashed line trace to the plot
interactive_combined_plot <- interactive_combined_plot %>%
  plotly::add_trace(
    x = red_line_trace$x,
    y = red_line_trace$y,
    mode = red_line_trace$mode,
    line = red_line_trace$line,
    text = red_line_trace$text,
    hoverinfo = red_line_trace$hoverinfo,
    showlegend = red_line_trace$showlegend
  )

# View the interactive plot
interactive_combined_plot

Note: The significant rise in accidental poisoning deaths reflects the growing impact of synthetic opioids and poly-drug use in Scotland’s overdose crisis.

Figure 4: Drug Misuse Deaths Over Time by Cause (Pre- and Post-2011)

The navy dashed line indicates the 2011 policy change impacting reporting practices.
- Accidental poisoning became the leading cause of death post-2011, while deaths attributed to mental disorders decreased significantly.
- Other categories, including self-poisoning, undetermined deaths, and assault, exhibit less consistent trends, highlighting the multifaceted nature of drug-related mortality in Scotland.

# Create the faceted ggplot with different scales for each cause of death
faceted_plot <- ggplot(table_2_long, aes(x = year, y = deaths, color = cause_of_death, group = cause_of_death)) +
  # Add lines and points for causes of death
  geom_line(size = 1) +
  geom_point(size = 2) +
  # Add the policy line
  geom_vline(xintercept = 2011, linetype = "dashed", color = "navy", size = 0.8) +
  labs(
    title = "Drug Misuse Deaths Over Time by Cause (Pre- and Post-2011)",
    x = "Year",
    y = "Number of Deaths",
    color = "Cause of Death"
  ) +
  facet_wrap(~ cause_of_death, scales = "free_y") +  # Separate panels with free y-axis scales
  theme_minimal() +
  theme(
    strip.text = element_text(size = 12, face = "bold"),  # Facet titles
    legend.position = "none",  # Remove the legend
    plot.title = element_text(size = 16, face = "bold"),
    axis.title = element_text(size = 12),
    axis.text = element_text(size = 10)
  ) +
  aes(text = paste(
    "Year:", year,
    "<br>Deaths:", deaths,
    "<br>Cause of Death:", cause_of_death
  ))  # Add custom hover text

# Convert to interactive plotly object
interactive_faceted_plot <- ggplotly(faceted_plot, tooltip = "text") %>%
  layout(
    title = list(text = "Drug Misuse Deaths Over Time by Cause (Pre- and Post-2011)"),
    hoverlabel = list(font = list(size = 12))  # Adjust hover text size
  )

# View the interactive plot
interactive_faceted_plot

Self-poisoning and deaths of undetermined intent, also visualised in Figure 4, highlight the intersection between drug misuse and mental health crises. Scotland’s failure to integrate mental health and addiction services exacerbates these risks, leaving vulnerable individuals without adequate support.

Furthermore, there appears to be a growing role of poly-drug use, which complicates prevention efforts.
- Polydrug use contributes to over 70% of deaths (Figure 5), significantly increasing the risk of fatal interactions.
- This dominance of poly-drug use reveals shortcomings in current treatment models, which often address single-substance dependency rather than the interplay of multiple drugs (Ives and Ghelani, 2006).

Figure 5: Single-Drug vs. Poly-Drug Deaths in 2023

Poly-drug deaths accounted for 73.7% (864) of total drug-related deaths, highlighting the complexity and heightened risks associated with substance interactions.
Single-drug deaths represented 26.3% (308) of the total, underscoring the need for interventions targeting poly-drug use specifically.

# Load and clean necessary data for both single-drug and poly-drug deaths in 2023
table_8 <- read.xlsx(file_path, sheet = "Table_8", startRow = 5) %>%
  clean_names()

table_3 <- read.xlsx(file_path, sheet = "Table_3", startRow = 6) %>%
  clean_names()

# Filter for 2023 data
single_drug_2023 <- table_8 %>%
  filter(!grepl("all drug misuse deaths", tolower(drugs_implicated), fixed = TRUE)) %>%  # Exclude total row
  summarize(single_drug_deaths = sum(persons, na.rm = TRUE)) %>%
  pull(single_drug_deaths)

total_deaths_2023 <- table_3 %>%
  filter(year == 2023) %>%
  pull(all_drug_misuse_deaths)

# Calculate poly-drug deaths and create a summary data frame
poly_drug_2023 <- total_deaths_2023 - single_drug_2023

drug_summary_2023 <- data.frame(
  type = c("Single-Drug Deaths", "Poly-Drug Deaths"),
  deaths = c(single_drug_2023, poly_drug_2023)
) %>%
  mutate(percentage = round(deaths / sum(deaths) * 100, 1))

# Plot the summary data
poly_vs_single_plot <- ggplot(drug_summary_2023, aes(x = deaths, y = type, fill = type)) +
  geom_bar(stat = "identity") +
  geom_text(aes(
    label = paste0(type, "\nDeaths: ", deaths, "\nPercentage: ", percentage, "%"),
    x = deaths / 2  # Position text at the middle of each bar
  ),
  color = "white",    # Text color for contrast
  size = 5,           # Font size for text
  hjust = 0.5         # Center horizontally within the bar
  ) +
  labs(
    title = "Single-Drug vs Poly-Drug Deaths (2023)",
    x = "Number of Deaths",
    y = NULL,          # Remove Y-axis label
    fill = NULL        # Remove legend title
  ) +
  theme_minimal() +
  theme(
    axis.text.y = element_blank(),        # Remove Y-axis text
    axis.ticks.y = element_blank(),       # Remove Y-axis ticks
    legend.position = "none",             # Remove legend
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),  # Centered title
    axis.title.x = element_text(size = 12)  # X-axis label size
  ) +
  scale_fill_brewer(palette = "Set2")  # Use Set2 color palette

poly_vs_single_plot

Addressing these causes requires interventions that extend beyond harm reduction.

The widespread use of methadone for opioid dependency, dispensed by 79.1% of pharmacies in 2006, demonstrates a commitment to harm reduction.
However, the lack of progress in needle exchange services remains a significant gap in addressing drug misuse effectively.

Socioeconomic Disparities

Socioeconomic deprivation is a key driver of drug misuse deaths in Scotland:

Individuals in the most deprived quintile (SIMD 1) experience death rates up to eight times higher than those in the least deprived quintile (SIMD 5).
Factors such as the affordability and accessibility of ‘street benzos’ in deprived areas exacerbate this crisis (McAuley et al., 2022).

Addressing these disparities requires structural reforms to ensure:
1. Equitable funding for harm reduction services.
2. Improved access to essential interventions in the most deprived areas.

Figure 6: Drug Misuse Deaths Over Time by SIMD Quintile (2000–2023)

Age-standardized rates highlight stark disparities:
- Individuals in the most deprived quintile (SIMD 1) consistently experience the highest rates of drug misuse deaths.
- In contrast, the least deprived quintile (SIMD 5) shows significantly lower rates, underscoring the impact of socioeconomic inequalities.

# Load and clean Table 9 (Quintiles)
table_9 <- read.xlsx(file_path, sheet = "Table_9", startRow = 5) %>%
  clean_names()

quintile_data <- table_9 %>%
  select(year, starts_with("simd_quintile")) %>%
  pivot_longer(cols = -year, names_to = "quintile", values_to = "age_standardized_rate") %>%
  mutate(quintile = gsub("simd_quintile_", "", quintile))

quintile_data_clean <- quintile_data %>%
  filter(grepl("age_standardised_rate", quintile)) %>%
  mutate(
    quintile_number = case_when(
      grepl("1_most_deprived", quintile) ~ "1 (Most Deprived)",
      grepl("5_least_deprived", quintile) ~ "5 (Least Deprived)",
      TRUE ~ gsub("_age_standardised_rate", "", quintile)
    )
  ) %>%
  select(year, quintile_number, age_standardized_rate)

quintile_data_clean %>% filter(is.na(age_standardized_rate))

#Create Quintile Line plot  
quintile_plot <- ggplot(quintile_data_clean, aes(x = year, y = age_standardized_rate, color = quintile_number, group = quintile_number)) +
  geom_line(size = 1.2) +
  geom_point(size = 1.5, alpha = 0.8) +
  labs(
    title = "Drug Misuse Deaths Over Time by SIMD Quintile",
    subtitle = "Highlighting disparities in age-standardized rates (2000–2023).",
    x = "Year",
    y = "Age-Standardized Rate (per 100,000)",
    color = "Quintile"
  ) +
  theme_minimal() +
  theme(
    legend.position = "right",
    legend.title = element_text(size = 10),
    legend.text = element_text(size = 9)
  ) +
  scale_color_brewer(palette = "Set2") + # Vibrant color palette
  aes(text = paste("Year:", year, "<br>Age-Standardized Rate:", round(age_standardized_rate, 2), "<br>Quintile:", quintile_number))  # Custom hover text

# Convert to interactive plot
quintile_interactive_plot <- ggplotly(quintile_plot, tooltip = "text")

# View Quintile Interactive Plot
quintile_interactive_plot

Key Takeaway:
Addressing the stark socioeconomic disparities requires prioritizing harm reduction services in the most deprived communities. Policies should specifically target barriers to access, including:
- Affordability of services.
- Stigma associated with drug misuse.
- Equitable distribution of harm reduction services.

The funding formula used by NHS Scotland Boards and Alcohol and Drug Partnerships (ADPs) has disproportionately impacted deprived communities, leading to:
- Increased Drug-Related Death (DRD) rates (McPhee and Sheridan, 2020).
- Limited participation of pharmacies in needle exchange programs due to:
- Safety concerns.
- Perceived impact on customer relationships.

Despite modest improvements, only 12.5% of Scottish pharmacies provided needle exchange services as of 2006, reflecting the persistent challenges in achieving equitable service distribution (Matheson, Bond, and Tinelli, 2007).

Figure 7: Drug Misuse Deaths Over Time by SIMD Decile (2000–2023)

Age-standardized rates reveal stark inequalities:
- The most deprived decile (1) consistently experiences the highest rates of drug misuse deaths, peaking in 2020.
- In contrast, the least deprived decile (10) shows consistently low rates throughout the period.
- These trends highlight the socioeconomic gradient in drug misuse mortality and the urgent need for targeted interventions.

# Load and clean Table 10 (Deciles) just once
table_10 <- read.xlsx(file_path, sheet = "Table_10", startRow = 6) %>%
  clean_names()

# Process the decile data in a single step
decile_data <- table_10 %>%
  select(year, starts_with("simd_decile")) %>%
  mutate(across(starts_with("simd_decile"), ~ as.numeric(gsub("[^0-9.]", "", .)))) %>%
  pivot_longer(cols = -year, names_to = "decile", values_to = "age_standardized_rate") %>%
  mutate(decile = gsub("simd_decile_", "", decile))

# Clean and filter the decile data
decile_data_clean <- decile_data %>%
  separate(decile, into = c("decile_number", "metric"), sep = "_(?=[^_]+$)", extra = "merge", fill = "right") %>%
  filter(metric == "rate") %>%
  mutate(
    decile_number = gsub("_most_deprived", "", decile_number),
    decile_number = gsub("_age_standardised", "", decile_number),
    decile_number = gsub("_least_deprived", "", decile_number),
    decile_number = as.factor(decile_number),
    decile_label = case_when(
      decile_number == "1" ~ "1 (Most Deprived)",
      decile_number == "10" ~ "10 (Least Deprived)",
      TRUE ~ as.character(decile_number)
    )
  ) %>%
  filter(!is.na(age_standardized_rate))

# Manually assign colors for deciles 1 to 10 (Set2 color palette)
decile_colors <- c(RColorBrewer::brewer.pal(8, "Set2"), "#Bd82ff", "#A0c4ff")

# Create the decile plot
decile_plot <- ggplot(decile_data_clean, aes(x = year, y = age_standardized_rate, color = decile_number, group = decile_number)) +
  geom_line(data = decile_data_clean %>% filter(decile_number %in% c("1", "10")), aes(color = decile_number), size = 1.2) + 
  geom_line(data = decile_data_clean %>% filter(!decile_number %in% c("1", "10")), aes(color = decile_number), alpha = 0.7, size = 0.7) +
  geom_point(size = 1.5, alpha = 0.6) +
  labs(
    title = "Drug Misuse Deaths Over Time by SIMD Decile",
    subtitle = "Highlighting disparities in age-standardized rates (2000–2023).",
    x = "Year",
    y = "Age-Standardized Rate (per 100,000)",
    color = "SIMD Decile"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(size = 14, face = "bold"),
    plot.subtitle = element_text(size = 12, face = "italic"),
    legend.title = element_text(size = 11),
    legend.text = element_text(size = 10)
  ) +
  scale_color_manual(values = decile_colors, name = "SIMD Decile", breaks = 1:10, labels = c("1 (Most Deprived)", "2", "3", "4", "5", "6", "7", "8", "9", "10 (Least Deprived)")) +
  aes(text = paste("Year:", year, "<br>Age-Standardized Rate:", round(age_standardized_rate, 2), "<br>Decile:", decile_label))

# Convert to interactive plot
decile_interactive_plot <- ggplotly(decile_plot, tooltip = "text")

# View Decile Interactive Plot
decile_interactive_plot

Policy responses have been inadequate in addressing the structural drivers of drug misuse. Critiques of the governance of drug and alcohol services since 2009 emphasize the following issues:
- Widened inequalities due to funding decisions.
- Disproportionate risks for already vulnerable communities (McPhee and Sheridan, 2020).

Governance failures undermine efforts to tackle the systemic drivers of drug misuse and drug-related deaths (DRDs). Key challenges include:
- Concentration of needle exchange services in urban areas, limited further by:
- Stigma.
- Resource constraints (Matheson, Bond, and Tinelli, 2007) (Childs et al., 2021).

Addressing these disparities requires:
- Investments in affordable housing.
- Initiatives for job creation.
- Development of integrated, community-based health services.

Geographic Patterns

Geographic differences in drug misuse reflect Scotland’s uneven distribution of social and health resources:

Rural areas, which accounted for only 18.1% of surveyed pharmacies in 2006, face significant limitations in harm reduction service availability.
- This underscores the need for targeted interventions to bridge geographic disparities in service provision (Matheson, Bond, and Tinelli, 2007).
Urban areas like Glasgow experience higher rates of drug deaths due to the intersection of:
- Homelessness.
- Poverty.
- Social exclusion (Figure 8).
In contrast, rural areas report lower rates but encounter challenges like:
- Geographic isolation.
- Limited harm reduction services (Bailey, Bramley, and Gannon, 2016).

Figure 8: Total Drug Misuse Deaths by Health Board Area in Scotland (2010–2023)

The map illustrates geographic disparities in drug misuse deaths:
- Urban areas, particularly Greater Glasgow and Clyde, exhibit the highest concentration of deaths, shown in dark blue.
- Rural and less densely populated regions report significantly lower totals.
- These trends emphasize regional inequalities in drug misuse mortality.

#import health boards 
hb_shapes <- st_read("NHS_healthboards_2019.shp") %>% 
  clean_names()

#join hb shape data with hb deaths 
table_HB1 <- read.xlsx(file_path, sheet = "Table_HB1", startRow = 5)

persons_hb1 <- table_HB1 %>% 
  filter(Sex == "Persons") %>%
  select(-Scotland)

long_HB1 <- persons_hb1 %>% 
  pivot_longer(
    cols = c(Ayrshire.and.Arran:Western.Isles), 
    names_to = "hb_name", 
    values_to = "deaths" 
  ) 

# Recode specific health board names in the `hb_name` column
long_HB1 <- long_HB1 %>%
  mutate(hb_name = recode(hb_name,
    "Ayrshire.and.Arran" = "Ayrshire and Arran", 
    "Dumfries.and.Galloway" = "Dumfries and Galloway", 
    "Greater.Glasgow.and.Clyde" = "Greater Glasgow and Clyde", 
    "Forth.Valley" = "Forth Valley", 
    "Western.Isles" = "Western Isles"
  ))


hb_deaths <- long_HB1 %>% 
  full_join(hb_shapes, by = "hb_name")

# Ensure the data is in the correct format for plotting (sf object)
hb_deaths_sf <- st_as_sf(hb_deaths)

# Summarize total deaths by region (Health Board) and year
total_deaths_by_region <- hb_deaths_sf %>%
  group_by(hb_name) %>%
  summarize(total_deaths = sum(deaths, na.rm = TRUE))

# Ensure the data is in WGS84 for compatibility with leaflet
hb_deaths_sf <- st_transform(hb_deaths_sf, crs = 4326)

# Summarize total deaths by region (Health Board)
total_deaths_by_region <- hb_deaths_sf %>%
  group_by(hb_name) %>%
  summarize(total_deaths = sum(deaths, na.rm = TRUE), .groups = "drop")

# Create the interactive leaflet map
hb_map <- leaflet(total_deaths_by_region) %>%
  addTiles() %>%
  addPolygons(
    fillColor = ~colorNumeric("YlOrRd", total_deaths)(total_deaths),
    weight = 1,
    opacity = 1,
    color = "white",
    fillOpacity = 0.7,
    popup = ~paste(
      "<strong>Health Board:</strong>", hb_name,
      "<br><strong>Total Deaths:</strong>", total_deaths
    )
  ) %>%
  addLegend(
    pal = colorNumeric("YlOrRd", total_deaths_by_region$total_deaths),
    values = total_deaths_by_region$total_deaths,
    title = "<b>Total Deaths</b>",
    position = "bottomright"
  ) %>%
  addScaleBar(
    position = "bottomleft",
    options = scaleBarOptions(imperial = FALSE)
  ) %>%
  addControl(
    HTML("<div style='background-color: white; padding: 10px; border-radius: 5px; box-shadow: 0px 0px 5px gray;'>
            <h3 style='margin: 0;'>Interactive Map of Drug Misuse Deaths in Scotland (2010–2023)</h3>
          </div>"),
    position = "topright"
  ) %>%
  addControl(
    HTML("<div style='background-color: white; padding: 10px; border-radius: 5px; box-shadow: 0px 0px 5px gray;'>
            <p style='margin: 0;'>Note: Data reflects total drug misuse deaths reported by region from 2010 to 2023.</p>
          </div>"),
    position = "bottomleft"
  )

hb_map

Expanding harm reduction services in high-risk regions like Glasgow, such as mobile units or supervised injection sites, can help reduce the geographic disparity in drug-related deaths.

Critically, the allocation of resources has not aligned with regional needs. Due to Glasgow’s high mortality rates (Figure 8), funding for harm reduction services in the area has been insufficient to meet demand (Tweed et al., 2018).
- The expansion of mobile harm reduction units and supervised injection facilities could address some of these disparities.
- By doing so, Scotland would be one step closer to ensuring its vulnerable populations have access to life-saving interventions.

Poly-Drug Use vs. Single-Drug Use

Over 70% of drug-related deaths in 2023 were linked to poly-drug use (Figure 5).
Current treatment models, which focus on single-substance dependencies, are inadequate.

Key Issues and Risks

Poly-drug users frequently use benzodiazepines, particularly etizolam, which is implicated in fatal overdoses.
- Only 1% of deaths involved etizolam alone (McAuley et al., 2022).
The combination of substances, especially benzodiazepines and opioids, dramatically increases the risk of fatal overdose (Boon et al., 2020).

Interventions Needed

Expand detoxification and rehabilitation programs that address interactions between multiple substances.
Improve education on the risks of poly-drug use.
- Distribute naloxone alongside targeted public health campaigns to warn of the risks associated with combining substances.

Policy Implications and Recommendations

Expanding Harm Reduction Services

Invest in supervised injection sites, mobile harm reduction units, and needle exchange programs.

Addressing Systemic Inequities

Implement equitable funding for deprived regions.
Integrate mental health and addiction services.

Targeted Public Health Campaigns

Promote education on the risks of poly-drug use.
Expand naloxone distribution programs and training initiatives.

Conclusion

Scotland’s drug misuse deaths result from a complex interplay of socioeconomic, behavioural, and policy failures. Over the past three decades, systemic shortcomings have fuelled one of Europe’s highest drug-related death rates.

Key Challenges

Persistent poverty and untreated mental health crises.
Inadequate harm reduction services.
Governance failures and inequitable funding practices (McPhee and Sheridan, 2020).

Recommendations

Sustained investment in harm reduction strategies.
Structural reforms to ensure equitable access to services.
Integration of mental health and addiction care.

The success of the National Naloxone Programme demonstrates the potential of targeted, well-funded interventions to reduce high-risk mortality (Bird et al., 2016).
- However, such efforts must be scaled and complemented by broader public health initiatives that prioritise prevention, equity, and long-term community resilience.

By adopting a proactive, equity-focused approach, Scotland can pave the way for lasting systemic change, ultimately saving lives and improving community resilience.

Sources and References

Data Source

National Records of Scotland (2023) Drug-related deaths in Scotland in 2023. Available at: National Records of Scotland

Mapping Scotland’s Drug Death Crisis: An Analysis of Trends, Disparities, and Policy Failures

Alexandra Anderson